Center for Computational Neuroscience, Flatiron Institute, New York, USA.
John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, USA.
Biol Cybern. 2022 Dec;116(5-6):557-568. doi: 10.1007/s00422-022-00943-8. Epub 2022 Sep 7.
An important problem in neuroscience is to understand how brains extract relevant signals from mixtures of unknown sources, i.e., perform blind source separation. To model how the brain performs this task, we seek a biologically plausible single-layer neural network implementation of a blind source separation algorithm. For biological plausibility, we require the network to satisfy the following three basic properties of neuronal circuits: (i) the network operates in the online setting; (ii) synaptic learning rules are local; and (iii) neuronal outputs are nonnegative. Closest is the work by Pehlevan et al. (Neural Comput 29:2925-2954, 2017), which considers nonnegative independent component analysis (NICA), a special case of blind source separation that assumes the mixture is a linear combination of uncorrelated, nonnegative sources. They derive an algorithm with a biologically plausible 2-layer network implementation. In this work, we improve upon their result by deriving 2 algorithms for NICA, each with a biologically plausible single-layer network implementation. The first algorithm maps onto a network with indirect lateral connections mediated by interneurons. The second algorithm maps onto a network with direct lateral connections and multi-compartmental output neurons.
神经科学中的一个重要问题是了解大脑如何从未知来源的混合物中提取相关信号，即执行盲源分离。为了模拟大脑如何执行这项任务，我们寻求盲源分离算法的生物上合理的单层神经网络实现。为了具有生物合理性，我们要求网络满足神经元电路的以下三个基本属性：（i）网络在在线设置中运行；（ii）突触学习规则是局部的；（iii）神经元输出是非负的。最接近的是 Pehlevan 等人的工作（Neural Comput 29:2925-2954, 2017），它考虑了非负独立分量分析（NICA），这是盲源分离的一种特殊情况，假设混合物是不相关的、非负源的线性组合。他们提出了一种具有生物合理性的 2 层网络实现的算法。在这项工作中，我们通过为 NICA 导出 2 种具有生物合理性的单层网络实现算法来改进他们的结果。第一种算法映射到一个具有由中间神经元介导的间接横向连接的网络上。第二种算法映射到具有直接横向连接和多室输出神经元的网络上。
